Refining Ground Truth Poses in Autonomous Driving Datasets via Neural Rendering

Quentin Herau; Nathan Piasco; Moussab Bennehar; Luis Roldão; Dzmitry Tsishkou; Bingbing Liu; Cyrille Migniot; Pascal Vasseur; Cédric Demonceaux

doi:10.1109/lra.2026.3732898

arXiv · 2504.15776

Refining Ground Truth Poses in Autonomous Driving Datasets via Neural Rendering

Quentin Herau·Nathan Piasco·Moussab Bennehar·Luis Roldão·Dzmitry Tsishkou·Bingbing Liu·Cyrille Migniot·Pascal Vasseur·Cédric Demonceaux

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Abstract

Public autonomous driving datasets underpin the training and benchmarking of perception, mapping, and localization algorithms, yet residual inaccuracies in sensor calibration and ego-poses can silently degrade both model performance and evaluation reliability. We introduce MOISST++, a Neural Radiance Field (NeRF)-based pipeline that jointly refines extrinsic sensor calibration and continuous-time ego-trajectories at dataset scale. The method optimizes shared rig parameters across multiple subsequences and corrects per-subsequence trajectories via a learned continuous-time correction, going beyond prior work that targets individual scenes. We validate pose improvements without ground truth through a complementary evaluation suite combining Structure from Motion (SfM) triangulation, novel view synthesis, and multi-modal geometric consistency metrics, verify their coherence via cross-metric agreement, and confirm their sensitivity through a controlled-perturbation study with known injected errors. Applied to four major datasets (KITTI-360, nuScenes, PandaSet, and Waymo), MOISST++ yields statistically significant improvements on most metrics on nuScenes, PandaSet and Waymo, and marginal, within-noise changes on the already well-calibrated KITTI-360. We publicly release the optimized poses and calibration parameters, together with our evaluation code, to support more reliable research and benchmarking.

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Quentin Herau, Nathan Piasco, Moussab Bennehar, Luis Roldão, Dzmitry Tsishkou, Bingbing Liu, Cyrille Migniot, Pascal Vasseur, Cédric Demonceaux. 2026-09-18. Refining Ground Truth Poses in Autonomous Driving Datasets via Neural Rendering. https://doi.org/10.1109/lra.2026.3732898

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